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setfit-bert

Text Classification
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bert
generated_from_setfit_trainer
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---
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: If the Probable Cause Committee determines that charges should be filed, the
respondent is notified of the specific nature of the charges and the Board's proposed
settlement of the issues. Said notice shall be sent by certified mail, return
receipt requested, to the respondent's last known address. If a hearing is to
be scheduled, the notice shall be sent by certified mail, return receipt requested,
to the respondent's last known address not less than ten (10) days before the
date of the scheduled hearing. The Board will conduct the hearing with the assistance
of a hearing officer, who will hear all competent and relevant evidence in support
of the charges. The hearing will be conducted in accordance with the Alabama Administrative
Procedures Act, Section 41-22-13, Code of Ala. 1975. Upon conclusion of the hearing,
the members of the Board (excluding the Probable Cause Committee Board member)
will determine the appropriate action to be taken, and shall notify, or cause
to be notified, the respondent of such action. If the Board suspends or revokes
a registration, or issues a reprimand or fine against the respondent, he or she
may appeal to the Circuit Court of Montgomery County, Alabama.
- text: Definitions governing the construction of this subchapter can be found in
Chapter 1, Section 790 of this subdivision.
- text: Any decision to deny, restrict or limit an inmate of any right, service, item
or article, guaranteed an inmate by the provisions of this Part, shall be done
in accordance with section 7075.5 of this Title.
- text: 'After a port drayage motor carrier has been placed on the public list, the
Labor Commissioner shall remove the motor carrier from the list within 15 business
days upon the following: (a) The Labor Commissioner''s Office determines after
review of submitted documents specified in subsections (1), (2), and (3) that
there has been full payment of an unsatisfied judgment or any other final liability
for all violations identified in Labor Code sections 2810.4(b)(1)(A)-(B) or that
the port drayage motor carrier has entered into an approved settlement dispensing
of the judgment or liability; or, in the case of a subsequent liability against
a prior offender, the prior offender prevailed in an appeal. (1) A port drayage
motor carrier shall present such proof by submitting a written statement under
penalty of perjury stating the basis for removal of the listing, along with the
accompanying documentation specified in subsections (2) and (3), as applicable,
by mail to the Labor Commissioner''s Office, Attn: SB 1402 Proof of Payment or
Settlement, 1500 Hughes Way, Suite C-202, Long Beach, CA 90810, or electronically
in pdf format via email to: [email protected]. (2) For purposes of sufficiently
documenting the payment or satisfaction of a judgment, tax assessment, or tax
lien or a citation or ODA, the port drayage motor carrier shall identify and provide
the documentation required under Section 13878, as applicable. (3) For purposes
of sufficiently documenting a disposition regarding a port drayage motor carrier
who is a prior offender who prevailed on appeal from a subsequent non-final judgment
or ruling or final citation or ODA, the motor carrier shall identify and provide
a conformed copy of the final judgment, ruling, citation, tax assessment, tax,
order, decision, or award which indicates the final disposition on the appeal.
(4) The port drayage motor carrier shall also provide documentation to show that
violations of any labor or employment law or regulation subject to a final judgment
or final citation or ODA have been sufficiently abated. This documentation shall
include: a statement under penalty of perjury that the port drayage motor carrier
does not currently engage in the labor practices identified as unlawful in the
final judgment, final citation or ODA, and a description of the steps the motor
carrier took to abate the violation(s). Subject to the Labor Commissioner''s request,
the agency may determine whether an applicable violation was abated by reviewing
any documents the motor carrier is required to maintain under the Labor Code,
wage orders, or any other applicable law. (b) The Labor Commissioner''s Office
will inform the port drayage motor carrier by letter of the agency''s determination
of whether the motor carrier has presented sufficient proof to merit removal from
the public list. (c) If a port drayage motor carrier on the public list has multiple
liability determinations posted on the public list, a separate request for removal
must be provided for each determination. Each removal request will be considered
individually and only the liability determination that is the subject of that
removal request may be removed.'
- text: '(Repealed). Author: Michael E. Mason, CPA'
inference: true
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 5000 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("rkoh/setfit-bert")
# Run inference
preds = model("(Repealed). Author: Michael E. Mason, CPA")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----------|:-----------------|:--------------|
| Word count | tensor(1) | tensor(370.1842) | tensor(52538) |
| Label | Training Sample Count |
|:-------------------------------|:----------------------|
| Purpose - Regulatory Objective | 0 |
| Scope and Applicability | 0 |
| Authority and Legal Basis | 0 |
| Administrative Details | 0 |
| Non-Purpose | 0 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0002 | 1 | 0.1006 | - |
| 0.0016 | 10 | 0.0759 | - |
| 0.0032 | 20 | 0.0767 | - |
| 0.0048 | 30 | 0.0852 | - |
| 0.0064 | 40 | 0.0765 | - |
| 0.008 | 50 | 0.078 | - |
| 0.0096 | 60 | 0.0734 | - |
| 0.0112 | 70 | 0.0687 | - |
| 0.0128 | 80 | 0.0566 | - |
| 0.0144 | 90 | 0.065 | - |
| 0.016 | 100 | 0.0583 | - |
| 0.0176 | 110 | 0.0584 | - |
| 0.0192 | 120 | 0.0466 | - |
| 0.0208 | 130 | 0.0661 | - |
| 0.0224 | 140 | 0.0583 | - |
| 0.024 | 150 | 0.0494 | - |
| 0.0256 | 160 | 0.0451 | - |
| 0.0272 | 170 | 0.0443 | - |
| 0.0288 | 180 | 0.0409 | - |
| 0.0304 | 190 | 0.0513 | - |
| 0.032 | 200 | 0.0415 | - |
| 0.0336 | 210 | 0.0413 | - |
| 0.0352 | 220 | 0.0478 | - |
| 0.0368 | 230 | 0.0319 | - |
| 0.0384 | 240 | 0.0273 | - |
| 0.04 | 250 | 0.0418 | - |
| 0.0416 | 260 | 0.0415 | - |
| 0.0432 | 270 | 0.0454 | - |
| 0.0448 | 280 | 0.0333 | - |
| 0.0464 | 290 | 0.0341 | - |
| 0.048 | 300 | 0.0504 | - |
| 0.0496 | 310 | 0.0296 | - |
| 0.0512 | 320 | 0.0293 | - |
| 0.0528 | 330 | 0.0263 | - |
| 0.0544 | 340 | 0.0292 | - |
| 0.056 | 350 | 0.0394 | - |
| 0.0576 | 360 | 0.0246 | - |
| 0.0592 | 370 | 0.0419 | - |
| 0.0608 | 380 | 0.0251 | - |
| 0.0624 | 390 | 0.02 | - |
| 0.064 | 400 | 0.0397 | - |
| 0.0656 | 410 | 0.0151 | - |
| 0.0672 | 420 | 0.0312 | - |
| 0.0688 | 430 | 0.0336 | - |
| 0.0704 | 440 | 0.0194 | - |
| 0.072 | 450 | 0.0251 | - |
| 0.0736 | 460 | 0.0167 | - |
| 0.0752 | 470 | 0.0203 | - |
| 0.0768 | 480 | 0.0158 | - |
| 0.0784 | 490 | 0.0165 | - |
| 0.08 | 500 | 0.0181 | - |
| 0.0816 | 510 | 0.0153 | - |
| 0.0832 | 520 | 0.0301 | - |
| 0.0848 | 530 | 0.0243 | - |
| 0.0864 | 540 | 0.0271 | - |
| 0.088 | 550 | 0.0185 | - |
| 0.0896 | 560 | 0.0221 | - |
| 0.0912 | 570 | 0.0171 | - |
| 0.0928 | 580 | 0.0284 | - |
| 0.0944 | 590 | 0.0335 | - |
| 0.096 | 600 | 0.0163 | - |
| 0.0976 | 610 | 0.0199 | - |
| 0.0992 | 620 | 0.0212 | - |
| 0.1008 | 630 | 0.0253 | - |
| 0.1024 | 640 | 0.0173 | - |
| 0.104 | 650 | 0.0376 | - |
| 0.1056 | 660 | 0.0135 | - |
| 0.1072 | 670 | 0.0216 | - |
| 0.1088 | 680 | 0.0279 | - |
| 0.1104 | 690 | 0.0126 | - |
| 0.112 | 700 | 0.0144 | - |
| 0.1136 | 710 | 0.0149 | - |
| 0.1152 | 720 | 0.0186 | - |
| 0.1168 | 730 | 0.0084 | - |
| 0.1184 | 740 | 0.0231 | - |
| 0.12 | 750 | 0.0152 | - |
| 0.1216 | 760 | 0.0174 | - |
| 0.1232 | 770 | 0.0235 | - |
| 0.1248 | 780 | 0.0144 | - |
| 0.1264 | 790 | 0.0081 | - |
| 0.128 | 800 | 0.0209 | - |
| 0.1296 | 810 | 0.014 | - |
| 0.1312 | 820 | 0.0208 | - |
| 0.1328 | 830 | 0.0146 | - |
| 0.1344 | 840 | 0.0159 | - |
| 0.136 | 850 | 0.0119 | - |
| 0.1376 | 860 | 0.0251 | - |
| 0.1392 | 870 | 0.0153 | - |
| 0.1408 | 880 | 0.0077 | - |
| 0.1424 | 890 | 0.0136 | - |
| 0.144 | 900 | 0.0131 | - |
| 0.1456 | 910 | 0.0058 | - |
| 0.1472 | 920 | 0.0146 | - |
| 0.1488 | 930 | 0.0186 | - |
| 0.1504 | 940 | 0.014 | - |
| 0.152 | 950 | 0.0127 | - |
| 0.1536 | 960 | 0.0074 | - |
| 0.1552 | 970 | 0.0246 | - |
| 0.1568 | 980 | 0.0137 | - |
| 0.1584 | 990 | 0.0061 | - |
| 0.16 | 1000 | 0.0067 | - |
| 0.1616 | 1010 | 0.0125 | - |
| 0.1632 | 1020 | 0.01 | - |
| 0.1648 | 1030 | 0.0116 | - |
| 0.1664 | 1040 | 0.0098 | - |
| 0.168 | 1050 | 0.0116 | - |
| 0.1696 | 1060 | 0.0051 | - |
| 0.1712 | 1070 | 0.0014 | - |
| 0.1728 | 1080 | 0.0056 | - |
| 0.1744 | 1090 | 0.0009 | - |
| 0.176 | 1100 | 0.0074 | - |
| 0.1776 | 1110 | 0.0019 | - |
| 0.1792 | 1120 | 0.0022 | - |
| 0.1808 | 1130 | 0.0063 | - |
| 0.1824 | 1140 | 0.0059 | - |
| 0.184 | 1150 | 0.0065 | - |
| 0.1856 | 1160 | 0.0151 | - |
| 0.1872 | 1170 | 0.0034 | - |
| 0.1888 | 1180 | 0.0033 | - |
| 0.1904 | 1190 | 0.0085 | - |
| 0.192 | 1200 | 0.0041 | - |
| 0.1936 | 1210 | 0.0084 | - |
| 0.1952 | 1220 | 0.004 | - |
| 0.1968 | 1230 | 0.0148 | - |
| 0.1984 | 1240 | 0.0111 | - |
| 0.2 | 1250 | 0.0125 | - |
| 0.2016 | 1260 | 0.0086 | - |
| 0.2032 | 1270 | 0.0042 | - |
| 0.2048 | 1280 | 0.0041 | - |
| 0.2064 | 1290 | 0.0078 | - |
| 0.208 | 1300 | 0.0042 | - |
| 0.2096 | 1310 | 0.0078 | - |
| 0.2112 | 1320 | 0.0065 | - |
| 0.2128 | 1330 | 0.0079 | - |
| 0.2144 | 1340 | 0.0157 | - |
| 0.216 | 1350 | 0.0086 | - |
| 0.2176 | 1360 | 0.0057 | - |
| 0.2192 | 1370 | 0.0025 | - |
| 0.2208 | 1380 | 0.0057 | - |
| 0.2224 | 1390 | 0.0051 | - |
| 0.224 | 1400 | 0.0054 | - |
| 0.2256 | 1410 | 0.0048 | - |
| 0.2272 | 1420 | 0.01 | - |
| 0.2288 | 1430 | 0.0087 | - |
| 0.2304 | 1440 | 0.0053 | - |
| 0.232 | 1450 | 0.0046 | - |
| 0.2336 | 1460 | 0.004 | - |
| 0.2352 | 1470 | 0.0062 | - |
| 0.2368 | 1480 | 0.0088 | - |
| 0.2384 | 1490 | 0.0093 | - |
| 0.24 | 1500 | 0.0005 | - |
| 0.2416 | 1510 | 0.0074 | - |
| 0.2432 | 1520 | 0.0042 | - |
| 0.2448 | 1530 | 0.0072 | - |
| 0.2464 | 1540 | 0.0007 | - |
| 0.248 | 1550 | 0.005 | - |
| 0.2496 | 1560 | 0.002 | - |
| 0.2512 | 1570 | 0.001 | - |
| 0.2528 | 1580 | 0.0062 | - |
| 0.2544 | 1590 | 0.0004 | - |
| 0.256 | 1600 | 0.0009 | - |
| 0.2576 | 1610 | 0.0041 | - |
| 0.2592 | 1620 | 0.0119 | - |
| 0.2608 | 1630 | 0.0011 | - |
| 0.2624 | 1640 | 0.0104 | - |
| 0.264 | 1650 | 0.0037 | - |
| 0.2656 | 1660 | 0.0005 | - |
| 0.2672 | 1670 | 0.004 | - |
| 0.2688 | 1680 | 0.0036 | - |
| 0.2704 | 1690 | 0.0037 | - |
| 0.272 | 1700 | 0.0013 | - |
| 0.2736 | 1710 | 0.0004 | - |
| 0.2752 | 1720 | 0.0006 | - |
| 0.2768 | 1730 | 0.0065 | - |
| 0.2784 | 1740 | 0.0033 | - |
| 0.28 | 1750 | 0.0009 | - |
| 0.2816 | 1760 | 0.0117 | - |
| 0.2832 | 1770 | 0.0033 | - |
| 0.2848 | 1780 | 0.0032 | - |
| 0.2864 | 1790 | 0.0037 | - |
| 0.288 | 1800 | 0.0022 | - |
| 0.2896 | 1810 | 0.0011 | - |
| 0.2912 | 1820 | 0.0006 | - |
| 0.2928 | 1830 | 0.0007 | - |
| 0.2944 | 1840 | 0.0054 | - |
| 0.296 | 1850 | 0.0007 | - |
| 0.2976 | 1860 | 0.0035 | - |
| 0.2992 | 1870 | 0.0038 | - |
| 0.3008 | 1880 | 0.0075 | - |
| 0.3024 | 1890 | 0.0017 | - |
| 0.304 | 1900 | 0.0005 | - |
| 0.3056 | 1910 | 0.0002 | - |
| 0.3072 | 1920 | 0.0002 | - |
| 0.3088 | 1930 | 0.0002 | - |
| 0.3104 | 1940 | 0.0033 | - |
| 0.312 | 1950 | 0.0085 | - |
| 0.3136 | 1960 | 0.0004 | - |
| 0.3152 | 1970 | 0.0005 | - |
| 0.3168 | 1980 | 0.0002 | - |
| 0.3184 | 1990 | 0.003 | - |
| 0.32 | 2000 | 0.0007 | - |
| 0.3216 | 2010 | 0.0009 | - |
| 0.3232 | 2020 | 0.0003 | - |
| 0.3248 | 2030 | 0.0012 | - |
| 0.3264 | 2040 | 0.0086 | - |
| 0.328 | 2050 | 0.001 | - |
| 0.3296 | 2060 | 0.0009 | - |
| 0.3312 | 2070 | 0.0029 | - |
| 0.3328 | 2080 | 0.0033 | - |
| 0.3344 | 2090 | 0.0005 | - |
| 0.336 | 2100 | 0.0003 | - |
| 0.3376 | 2110 | 0.0033 | - |
| 0.3392 | 2120 | 0.0029 | - |
| 0.3408 | 2130 | 0.0001 | - |
| 0.3424 | 2140 | 0.0057 | - |
| 0.344 | 2150 | 0.0001 | - |
| 0.3456 | 2160 | 0.0002 | - |
| 0.3472 | 2170 | 0.004 | - |
| 0.3488 | 2180 | 0.002 | - |
| 0.3504 | 2190 | 0.0073 | - |
| 0.352 | 2200 | 0.0004 | - |
| 0.3536 | 2210 | 0.0006 | - |
| 0.3552 | 2220 | 0.0004 | - |
| 0.3568 | 2230 | 0.0032 | - |
| 0.3584 | 2240 | 0.007 | - |
| 0.36 | 2250 | 0.0096 | - |
| 0.3616 | 2260 | 0.0032 | - |
| 0.3632 | 2270 | 0.0006 | - |
| 0.3648 | 2280 | 0.0002 | - |
| 0.3664 | 2290 | 0.0032 | - |
| 0.368 | 2300 | 0.0002 | - |
| 0.3696 | 2310 | 0.0025 | - |
| 0.3712 | 2320 | 0.0002 | - |
| 0.3728 | 2330 | 0.0053 | - |
| 0.3744 | 2340 | 0.0017 | - |
| 0.376 | 2350 | 0.0013 | - |
| 0.3776 | 2360 | 0.0001 | - |
| 0.3792 | 2370 | 0.0032 | - |
| 0.3808 | 2380 | 0.0002 | - |
| 0.3824 | 2390 | 0.0019 | - |
| 0.384 | 2400 | 0.0015 | - |
| 0.3856 | 2410 | 0.0009 | - |
| 0.3872 | 2420 | 0.0006 | - |
| 0.3888 | 2430 | 0.0032 | - |
| 0.3904 | 2440 | 0.0033 | - |
| 0.392 | 2450 | 0.0003 | - |
| 0.3936 | 2460 | 0.0003 | - |
| 0.3952 | 2470 | 0.0016 | - |
| 0.3968 | 2480 | 0.0065 | - |
| 0.3984 | 2490 | 0.0011 | - |
| 0.4 | 2500 | 0.0032 | - |
| 0.4016 | 2510 | 0.0045 | - |
| 0.4032 | 2520 | 0.0001 | - |
| 0.4048 | 2530 | 0.0004 | - |
| 0.4064 | 2540 | 0.0001 | - |
| 0.408 | 2550 | 0.0027 | - |
| 0.4096 | 2560 | 0.0032 | - |
| 0.4112 | 2570 | 0.0034 | - |
| 0.4128 | 2580 | 0.0057 | - |
| 0.4144 | 2590 | 0.0029 | - |
| 0.416 | 2600 | 0.0008 | - |
| 0.4176 | 2610 | 0.0002 | - |
| 0.4192 | 2620 | 0.0033 | - |
| 0.4208 | 2630 | 0.0004 | - |
| 0.4224 | 2640 | 0.0057 | - |
| 0.424 | 2650 | 0.0001 | - |
| 0.4256 | 2660 | 0.0048 | - |
| 0.4272 | 2670 | 0.0043 | - |
| 0.4288 | 2680 | 0.0011 | - |
| 0.4304 | 2690 | 0.0053 | - |
| 0.432 | 2700 | 0.0001 | - |
| 0.4336 | 2710 | 0.0045 | - |
| 0.4352 | 2720 | 0.0032 | - |
| 0.4368 | 2730 | 0.0034 | - |
| 0.4384 | 2740 | 0.0031 | - |
| 0.44 | 2750 | 0.0065 | - |
| 0.4416 | 2760 | 0.0013 | - |
| 0.4432 | 2770 | 0.0027 | - |
| 0.4448 | 2780 | 0.0014 | - |
| 0.4464 | 2790 | 0.0036 | - |
| 0.448 | 2800 | 0.0009 | - |
| 0.4496 | 2810 | 0.0053 | - |
| 0.4512 | 2820 | 0.0001 | - |
| 0.4528 | 2830 | 0.0005 | - |
| 0.4544 | 2840 | 0.0006 | - |
| 0.456 | 2850 | 0.0015 | - |
| 0.4576 | 2860 | 0.0028 | - |
| 0.4592 | 2870 | 0.0006 | - |
| 0.4608 | 2880 | 0.0001 | - |
| 0.4624 | 2890 | 0.0024 | - |
| 0.464 | 2900 | 0.0012 | - |
| 0.4656 | 2910 | 0.0003 | - |
| 0.4672 | 2920 | 0.0028 | - |
| 0.4688 | 2930 | 0.0022 | - |
| 0.4704 | 2940 | 0.0002 | - |
| 0.472 | 2950 | 0.0006 | - |
| 0.4736 | 2960 | 0.0002 | - |
| 0.4752 | 2970 | 0.0034 | - |
| 0.4768 | 2980 | 0.0032 | - |
| 0.4784 | 2990 | 0.0001 | - |
| 0.48 | 3000 | 0.0001 | - |
| 0.4816 | 3010 | 0.0003 | - |
| 0.4832 | 3020 | 0.0001 | - |
| 0.4848 | 3030 | 0.0011 | - |
| 0.4864 | 3040 | 0.0001 | - |
| 0.488 | 3050 | 0.0003 | - |
| 0.4896 | 3060 | 0.0031 | - |
| 0.4912 | 3070 | 0.0032 | - |
| 0.4928 | 3080 | 0.0028 | - |
| 0.4944 | 3090 | 0.0032 | - |
| 0.496 | 3100 | 0.0002 | - |
| 0.4976 | 3110 | 0.0001 | - |
| 0.4992 | 3120 | 0.0008 | - |
| 0.5008 | 3130 | 0.0028 | - |
| 0.5024 | 3140 | 0.0001 | - |
| 0.504 | 3150 | 0.0001 | - |
| 0.5056 | 3160 | 0.0001 | - |
| 0.5072 | 3170 | 0.0007 | - |
| 0.5088 | 3180 | 0.0054 | - |
| 0.5104 | 3190 | 0.0001 | - |
| 0.512 | 3200 | 0.0001 | - |
| 0.5136 | 3210 | 0.0001 | - |
| 0.5152 | 3220 | 0.0001 | - |
| 0.5168 | 3230 | 0.0027 | - |
| 0.5184 | 3240 | 0.0001 | - |
| 0.52 | 3250 | 0.0028 | - |
| 0.5216 | 3260 | 0.0001 | - |
| 0.5232 | 3270 | 0.0001 | - |
| 0.5248 | 3280 | 0.0007 | - |
| 0.5264 | 3290 | 0.0001 | - |
| 0.528 | 3300 | 0.0001 | - |
| 0.5296 | 3310 | 0.0001 | - |
| 0.5312 | 3320 | 0.0001 | - |
| 0.5328 | 3330 | 0.004 | - |
| 0.5344 | 3340 | 0.0001 | - |
| 0.536 | 3350 | 0.0049 | - |
| 0.5376 | 3360 | 0.0034 | - |
| 0.5392 | 3370 | 0.0004 | - |
| 0.5408 | 3380 | 0.0001 | - |
| 0.5424 | 3390 | 0.001 | - |
| 0.544 | 3400 | 0.0023 | - |
| 0.5456 | 3410 | 0.0019 | - |
| 0.5472 | 3420 | 0.0001 | - |
| 0.5488 | 3430 | 0.0027 | - |
| 0.5504 | 3440 | 0.0002 | - |
| 0.552 | 3450 | 0.0016 | - |
| 0.5536 | 3460 | 0.0001 | - |
| 0.5552 | 3470 | 0.0001 | - |
| 0.5568 | 3480 | 0.0005 | - |
| 0.5584 | 3490 | 0.0 | - |
| 0.56 | 3500 | 0.0001 | - |
| 0.5616 | 3510 | 0.0001 | - |
| 0.5632 | 3520 | 0.0001 | - |
| 0.5648 | 3530 | 0.0001 | - |
| 0.5664 | 3540 | 0.003 | - |
| 0.568 | 3550 | 0.0001 | - |
| 0.5696 | 3560 | 0.0002 | - |
| 0.5712 | 3570 | 0.0001 | - |
| 0.5728 | 3580 | 0.0001 | - |
| 0.5744 | 3590 | 0.0002 | - |
| 0.576 | 3600 | 0.0 | - |
| 0.5776 | 3610 | 0.0001 | - |
| 0.5792 | 3620 | 0.0034 | - |
| 0.5808 | 3630 | 0.0001 | - |
| 0.5824 | 3640 | 0.0001 | - |
| 0.584 | 3650 | 0.0001 | - |
| 0.5856 | 3660 | 0.0001 | - |
| 0.5872 | 3670 | 0.0003 | - |
| 0.5888 | 3680 | 0.0031 | - |
| 0.5904 | 3690 | 0.0001 | - |
| 0.592 | 3700 | 0.0001 | - |
| 0.5936 | 3710 | 0.003 | - |
| 0.5952 | 3720 | 0.0002 | - |
| 0.5968 | 3730 | 0.0031 | - |
| 0.5984 | 3740 | 0.0001 | - |
| 0.6 | 3750 | 0.0035 | - |
| 0.6016 | 3760 | 0.0001 | - |
| 0.6032 | 3770 | 0.003 | - |
| 0.6048 | 3780 | 0.0033 | - |
| 0.6064 | 3790 | 0.0026 | - |
| 0.608 | 3800 | 0.0024 | - |
| 0.6096 | 3810 | 0.0002 | - |
| 0.6112 | 3820 | 0.0001 | - |
| 0.6128 | 3830 | 0.0001 | - |
| 0.6144 | 3840 | 0.0001 | - |
| 0.616 | 3850 | 0.0001 | - |
| 0.6176 | 3860 | 0.0022 | - |
| 0.6192 | 3870 | 0.0001 | - |
| 0.6208 | 3880 | 0.0004 | - |
| 0.6224 | 3890 | 0.0066 | - |
| 0.624 | 3900 | 0.0033 | - |
| 0.6256 | 3910 | 0.0001 | - |
| 0.6272 | 3920 | 0.0001 | - |
| 0.6288 | 3930 | 0.0001 | - |
| 0.6304 | 3940 | 0.0032 | - |
| 0.632 | 3950 | 0.0003 | - |
| 0.6336 | 3960 | 0.0031 | - |
| 0.6352 | 3970 | 0.0001 | - |
| 0.6368 | 3980 | 0.0001 | - |
| 0.6384 | 3990 | 0.0001 | - |
| 0.64 | 4000 | 0.0001 | - |
| 0.6416 | 4010 | 0.0003 | - |
| 0.6432 | 4020 | 0.0001 | - |
| 0.6448 | 4030 | 0.0029 | - |
| 0.6464 | 4040 | 0.0001 | - |
| 0.648 | 4050 | 0.0001 | - |
| 0.6496 | 4060 | 0.0029 | - |
| 0.6512 | 4070 | 0.0001 | - |
| 0.6528 | 4080 | 0.0001 | - |
| 0.6544 | 4090 | 0.0001 | - |
| 0.656 | 4100 | 0.0001 | - |
| 0.6576 | 4110 | 0.0001 | - |
| 0.6592 | 4120 | 0.0001 | - |
| 0.6608 | 4130 | 0.0001 | - |
| 0.6624 | 4140 | 0.0001 | - |
| 0.664 | 4150 | 0.0001 | - |
| 0.6656 | 4160 | 0.0023 | - |
| 0.6672 | 4170 | 0.0002 | - |
| 0.6688 | 4180 | 0.0002 | - |
| 0.6704 | 4190 | 0.0014 | - |
| 0.672 | 4200 | 0.0004 | - |
| 0.6736 | 4210 | 0.0035 | - |
| 0.6752 | 4220 | 0.0001 | - |
| 0.6768 | 4230 | 0.0005 | - |
| 0.6784 | 4240 | 0.0001 | - |
| 0.68 | 4250 | 0.0029 | - |
| 0.6816 | 4260 | 0.0001 | - |
| 0.6832 | 4270 | 0.0001 | - |
| 0.6848 | 4280 | 0.0001 | - |
| 0.6864 | 4290 | 0.0001 | - |
| 0.688 | 4300 | 0.0003 | - |
| 0.6896 | 4310 | 0.0002 | - |
| 0.6912 | 4320 | 0.0001 | - |
| 0.6928 | 4330 | 0.0 | - |
| 0.6944 | 4340 | 0.0 | - |
| 0.696 | 4350 | 0.0 | - |
| 0.6976 | 4360 | 0.0001 | - |
| 0.6992 | 4370 | 0.0 | - |
| 0.7008 | 4380 | 0.0 | - |
| 0.7024 | 4390 | 0.0 | - |
| 0.704 | 4400 | 0.0 | - |
| 0.7056 | 4410 | 0.0 | - |
| 0.7072 | 4420 | 0.0 | - |
| 0.7088 | 4430 | 0.0 | - |
| 0.7104 | 4440 | 0.0001 | - |
| 0.712 | 4450 | 0.0001 | - |
| 0.7136 | 4460 | 0.0 | - |
| 0.7152 | 4470 | 0.0 | - |
| 0.7168 | 4480 | 0.0001 | - |
| 0.7184 | 4490 | 0.0 | - |
| 0.72 | 4500 | 0.0 | - |
| 0.7216 | 4510 | 0.0 | - |
| 0.7232 | 4520 | 0.0 | - |
| 0.7248 | 4530 | 0.0 | - |
| 0.7264 | 4540 | 0.0001 | - |
| 0.728 | 4550 | 0.0058 | - |
| 0.7296 | 4560 | 0.0001 | - |
| 0.7312 | 4570 | 0.0002 | - |
| 0.7328 | 4580 | 0.0001 | - |
| 0.7344 | 4590 | 0.0 | - |
| 0.736 | 4600 | 0.0001 | - |
| 0.7376 | 4610 | 0.0001 | - |
| 0.7392 | 4620 | 0.0 | - |
| 0.7408 | 4630 | 0.0002 | - |
| 0.7424 | 4640 | 0.0 | - |
| 0.744 | 4650 | 0.0 | - |
| 0.7456 | 4660 | 0.0004 | - |
| 0.7472 | 4670 | 0.0 | - |
| 0.7488 | 4680 | 0.0001 | - |
| 0.7504 | 4690 | 0.0 | - |
| 0.752 | 4700 | 0.0 | - |
| 0.7536 | 4710 | 0.0001 | - |
| 0.7552 | 4720 | 0.0001 | - |
| 0.7568 | 4730 | 0.0 | - |
| 0.7584 | 4740 | 0.0037 | - |
| 0.76 | 4750 | 0.0001 | - |
| 0.7616 | 4760 | 0.0032 | - |
| 0.7632 | 4770 | 0.0 | - |
| 0.7648 | 4780 | 0.0 | - |
| 0.7664 | 4790 | 0.0001 | - |
| 0.768 | 4800 | 0.0031 | - |
| 0.7696 | 4810 | 0.0001 | - |
| 0.7712 | 4820 | 0.0002 | - |
| 0.7728 | 4830 | 0.0 | - |
| 0.7744 | 4840 | 0.0001 | - |
| 0.776 | 4850 | 0.0001 | - |
| 0.7776 | 4860 | 0.0002 | - |
| 0.7792 | 4870 | 0.0 | - |
| 0.7808 | 4880 | 0.0 | - |
| 0.7824 | 4890 | 0.0001 | - |
| 0.784 | 4900 | 0.0 | - |
| 0.7856 | 4910 | 0.0 | - |
| 0.7872 | 4920 | 0.0001 | - |
| 0.7888 | 4930 | 0.0 | - |
| 0.7904 | 4940 | 0.0 | - |
| 0.792 | 4950 | 0.0001 | - |
| 0.7936 | 4960 | 0.0 | - |
| 0.7952 | 4970 | 0.0001 | - |
| 0.7968 | 4980 | 0.0 | - |
| 0.7984 | 4990 | 0.0029 | - |
| 0.8 | 5000 | 0.0001 | - |
| 0.8016 | 5010 | 0.0 | - |
| 0.8032 | 5020 | 0.0001 | - |
| 0.8048 | 5030 | 0.0005 | - |
| 0.8064 | 5040 | 0.0 | - |
| 0.808 | 5050 | 0.0 | - |
| 0.8096 | 5060 | 0.0014 | - |
| 0.8112 | 5070 | 0.0031 | - |
| 0.8128 | 5080 | 0.0 | - |
| 0.8144 | 5090 | 0.0001 | - |
| 0.816 | 5100 | 0.0 | - |
| 0.8176 | 5110 | 0.0001 | - |
| 0.8192 | 5120 | 0.0001 | - |
| 0.8208 | 5130 | 0.0 | - |
| 0.8224 | 5140 | 0.0 | - |
| 0.824 | 5150 | 0.0001 | - |
| 0.8256 | 5160 | 0.0 | - |
| 0.8272 | 5170 | 0.0 | - |
| 0.8288 | 5180 | 0.0 | - |
| 0.8304 | 5190 | 0.0006 | - |
| 0.832 | 5200 | 0.006 | - |
| 0.8336 | 5210 | 0.0032 | - |
| 0.8352 | 5220 | 0.0001 | - |
| 0.8368 | 5230 | 0.0 | - |
| 0.8384 | 5240 | 0.0 | - |
| 0.84 | 5250 | 0.0 | - |
| 0.8416 | 5260 | 0.0031 | - |
| 0.8432 | 5270 | 0.0001 | - |
| 0.8448 | 5280 | 0.0017 | - |
| 0.8464 | 5290 | 0.0009 | - |
| 0.848 | 5300 | 0.0001 | - |
| 0.8496 | 5310 | 0.0001 | - |
| 0.8512 | 5320 | 0.0004 | - |
| 0.8528 | 5330 | 0.0 | - |
| 0.8544 | 5340 | 0.003 | - |
| 0.856 | 5350 | 0.0002 | - |
| 0.8576 | 5360 | 0.0001 | - |
| 0.8592 | 5370 | 0.0001 | - |
| 0.8608 | 5380 | 0.0 | - |
| 0.8624 | 5390 | 0.0001 | - |
| 0.864 | 5400 | 0.0001 | - |
| 0.8656 | 5410 | 0.0 | - |
| 0.8672 | 5420 | 0.0 | - |
| 0.8688 | 5430 | 0.0001 | - |
| 0.8704 | 5440 | 0.0 | - |
| 0.872 | 5450 | 0.0 | - |
| 0.8736 | 5460 | 0.0 | - |
| 0.8752 | 5470 | 0.0001 | - |
| 0.8768 | 5480 | 0.0 | - |
| 0.8784 | 5490 | 0.0 | - |
| 0.88 | 5500 | 0.0 | - |
| 0.8816 | 5510 | 0.0001 | - |
| 0.8832 | 5520 | 0.0 | - |
| 0.8848 | 5530 | 0.0 | - |
| 0.8864 | 5540 | 0.0 | - |
| 0.888 | 5550 | 0.0031 | - |
| 0.8896 | 5560 | 0.0 | - |
| 0.8912 | 5570 | 0.0001 | - |
| 0.8928 | 5580 | 0.0 | - |
| 0.8944 | 5590 | 0.0 | - |
| 0.896 | 5600 | 0.0 | - |
| 0.8976 | 5610 | 0.0001 | - |
| 0.8992 | 5620 | 0.0 | - |
| 0.9008 | 5630 | 0.0002 | - |
| 0.9024 | 5640 | 0.0031 | - |
| 0.904 | 5650 | 0.0 | - |
| 0.9056 | 5660 | 0.0 | - |
| 0.9072 | 5670 | 0.0 | - |
| 0.9088 | 5680 | 0.0001 | - |
| 0.9104 | 5690 | 0.0 | - |
| 0.912 | 5700 | 0.0 | - |
| 0.9136 | 5710 | 0.0 | - |
| 0.9152 | 5720 | 0.0032 | - |
| 0.9168 | 5730 | 0.0001 | - |
| 0.9184 | 5740 | 0.0024 | - |
| 0.92 | 5750 | 0.0 | - |
| 0.9216 | 5760 | 0.0 | - |
| 0.9232 | 5770 | 0.0017 | - |
| 0.9248 | 5780 | 0.0 | - |
| 0.9264 | 5790 | 0.0001 | - |
| 0.928 | 5800 | 0.0001 | - |
| 0.9296 | 5810 | 0.0 | - |
| 0.9312 | 5820 | 0.0 | - |
| 0.9328 | 5830 | 0.0 | - |
| 0.9344 | 5840 | 0.0 | - |
| 0.936 | 5850 | 0.0 | - |
| 0.9376 | 5860 | 0.0031 | - |
| 0.9392 | 5870 | 0.0 | - |
| 0.9408 | 5880 | 0.0 | - |
| 0.9424 | 5890 | 0.0 | - |
| 0.944 | 5900 | 0.0031 | - |
| 0.9456 | 5910 | 0.0 | - |
| 0.9472 | 5920 | 0.0 | - |
| 0.9488 | 5930 | 0.0 | - |
| 0.9504 | 5940 | 0.0 | - |
| 0.952 | 5950 | 0.0 | - |
| 0.9536 | 5960 | 0.0001 | - |
| 0.9552 | 5970 | 0.0 | - |
| 0.9568 | 5980 | 0.0 | - |
| 0.9584 | 5990 | 0.0031 | - |
| 0.96 | 6000 | 0.0001 | - |
| 0.9616 | 6010 | 0.0 | - |
| 0.9632 | 6020 | 0.0 | - |
| 0.9648 | 6030 | 0.0 | - |
| 0.9664 | 6040 | 0.0 | - |
| 0.968 | 6050 | 0.0 | - |
| 0.9696 | 6060 | 0.0 | - |
| 0.9712 | 6070 | 0.0 | - |
| 0.9728 | 6080 | 0.0027 | - |
| 0.9744 | 6090 | 0.0 | - |
| 0.976 | 6100 | 0.0031 | - |
| 0.9776 | 6110 | 0.003 | - |
| 0.9792 | 6120 | 0.0 | - |
| 0.9808 | 6130 | 0.0 | - |
| 0.9824 | 6140 | 0.0 | - |
| 0.984 | 6150 | 0.0 | - |
| 0.9856 | 6160 | 0.0 | - |
| 0.9872 | 6170 | 0.0 | - |
| 0.9888 | 6180 | 0.0028 | - |
| 0.9904 | 6190 | 0.0 | - |
| 0.992 | 6200 | 0.0 | - |
| 0.9936 | 6210 | 0.0 | - |
| 0.9952 | 6220 | 0.0 | - |
| 0.9968 | 6230 | 0.0 | - |
| 0.9984 | 6240 | 0.0 | - |
| 1.0 | 6250 | 0.0 | 0.0479 |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- Sentence Transformers: 3.2.0
- Transformers: 4.44.2
- PyTorch: 2.4.1+cu121
- Datasets: 3.0.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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